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Semi-supervised learning is designed to help reduce the cost of the manuallabelling process by exploiting the use of useful features from a largequantity of unlabelled data during training. Since pixel-level manual labellingin large-scale remote sensing imagery is expensive, semi-supervised learningbecomes an appropriate solution to this. However, most of the existingsemi-supervised learning methods still lack efficient perturbation methods topromote diversity of features and the precision of pseudo labels duringtraining. In order to fill this gap, we propose DiverseNet architectures whichexplore multi-head and multi-model semi-supervised learning algorithms bysimultaneously promoting precision and diversity during training. The twoproposed methods of DiverseNet, namely the DiverseHead and DiverseModel,achieve the highest semantic segmentation performance in four widely utilisedremote sensing imagery data sets compared to state-of-the-art semi-supervisedlearning methods. Meanwhile, the proposed DiverseHead architecture isrelatively lightweight in terms of parameter space compared to thestate-of-the-art methods whilst reaching high-performance results for all thetested data sets.

DiverseNet: Decision Diversified Semi-supervised Semantic Segmentation Networks for Remote Sensing Imagery